{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2b7da21-f761-4b32-9b9e-f8985e2f8dc9",
   "metadata": {},
   "source": [
    "Ch25\n",
    "# SymPy线性代数\n",
    "Book_1《编程不难》 | 鸢尾花书：从加减乘除到机器学习  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb1204b2-1f67-47e5-8b6b-890e797d193d",
   "metadata": {},
   "source": [
    "## 矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51ab495d-f96b-4f57-807a-e0145fa142de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\\\3 & 2 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 2, 3],\n",
       "[3, 2, 1]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import Matrix\n",
    "A = Matrix([[1, 2, 3], [3, 2, 1]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fa6e56c-ea2f-4aa7-ba3c-1586f1397adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import shape\n",
    "shape(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffa4887a-08d2-4e82-b487-0e887e6162ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 3\\\\2 & 2\\\\3 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 3],\n",
       "[2, 2],\n",
       "[3, 1]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fbbbc7c-d3aa-473d-b536-452bcbb59820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\\\3 & 2 & 1\\\\0 & 0 & -1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 2,  3],\n",
       "[3, 2,  1],\n",
       "[0, 0, -1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 增加一行\n",
    "Matrix([A, (0, 0, -1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4b1cc09-97f4-4512-b738-d1d3acad73ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\\\4 & 5 & 6\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 2, 3],\n",
       "[4, 5, 6]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定形状\n",
    "Matrix(2, 3, [1, 2, 3, 4, 5, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b284643-3499-40f2-8a62-880faf222f22",
   "metadata": {},
   "source": [
    "### 行向量、列向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46e673f9-666e-4075-9017-1878a262556c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1\\\\2\\\\3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1],\n",
       "[2],\n",
       "[3]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = Matrix([1, 2, 3])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29965118-d756-402a-b669-f998eef98246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[1, 2, 3]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d65d62f7-d2cc-47bc-9d4e-ec767b34fc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[1, 2, 3]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1, 2, 3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1884bd-53ed-4a8e-9dca-cfee0cdc7086",
   "metadata": {},
   "source": [
    "### 索引、切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36c076b6-68c0-46bd-a33e-672b96bb8979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1$"
      ],
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7358e2d0-146d-429b-8095-146e8889e7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1$"
      ],
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[-1,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67630f3f-bd38-457c-b167-8c4c10135219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[1, 2, 3]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7740c29-7a6b-49be-81aa-6b666e1c5845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1\\\\3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1],\n",
       "[3]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "611a6bdc-c549-4a7e-b2aa-fad268940fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[1, 2, 3]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.row(0)\n",
    "# A.row_del(0) 删除指定行\n",
    "# 插入指定行 M.row_insert(1, Matrix([[0, 4]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a799876-b059-42dd-a50d-a25aa71fafd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}3 & 2 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[3, 2, 1]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.row(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0f2c070-1ccd-4ca1-9037-cef84c655eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1\\\\3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1],\n",
       "[3]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.col(0)\n",
    "# A.col_del(0) 删除指定列\n",
    "# M.col_insert(0, Matrix([1, -2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29976d55-6a48-4ea9-8b6c-0c8246992eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}3\\\\1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[3],\n",
       "[1]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.col(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d35ca417-658d-41a0-b188-94cbd0b32852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2\\\\3 & 2\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 2],\n",
       "[3, 2]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[0:2, 0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f2a1f9-7fe1-4f14-8c9f-dd0c61b01b46",
   "metadata": {},
   "source": [
    "### 产生特殊矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7de910bc-ca1b-4501-a2c0-5ea64a416db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy.matrices import Matrix, eye, zeros, ones, diag, GramSchmidt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7d551ad-1787-49e2-9e11-cdeae3ccfd87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & 0\\\\0 & 1 & 0 & 0\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0, 0, 0],\n",
       "[0, 1, 0, 0],\n",
       "[0, 0, 1, 0],\n",
       "[0, 0, 0, 1]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da24cb14-2c90-48bf-965f-da5eb065203b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Matrix(2, 2, [0, 1, 1, 2])\n",
    "m.is_symmetric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8eeceea3-99f6-4310-968f-bd9bb3e36a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 0\\\\0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 0],\n",
       "[0, 0]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "501a9257-e9a8-4111-82f1-ee132eec8f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 0, 0, 0, 0],\n",
       "[0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros(2, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "be2b9c67-bcac-450e-aa05-d9e2d627c48f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & 1\\\\1 & 1 & 1\\\\1 & 1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 1, 1],\n",
       "[1, 1, 1],\n",
       "[1, 1, 1]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f74dc25d-b865-4117-89b2-7586809714cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & 1 & 1 & 1\\\\1 & 1 & 1 & 1 & 1\\\\1 & 1 & 1 & 1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 1, 1, 1, 1],\n",
       "[1, 1, 1, 1, 1],\n",
       "[1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones(3,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86b2bd20-841e-4edf-b5f1-46fc0c17f27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0\\\\0 & 1 & 0\\\\0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0, 0],\n",
       "[0, 1, 0],\n",
       "[0, 0, 1]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import eye\n",
    "eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ee26f455-2763-4d9a-91d1-85745f750c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import eye\n",
    "A = eye(3)\n",
    "A.is_diagonal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c35b87b-45ae-40e8-87a9-aae5d4b8a63a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 0, 0],\n",
       "[0, 0, 0],\n",
       "[0, 0, 0]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import zeros\n",
    "zeros(3, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44c3865b-51a1-4918-9501-adfcaf31cc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1\\\\1 & 1\\\\1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 1],\n",
       "[1, 1],\n",
       "[1, 1]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import ones\n",
    "ones(3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c4e0bf3a-ea65-4527-bee1-0e806e3ab07b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0\\\\0 & 2 & 0\\\\0 & 0 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0, 0],\n",
       "[0, 2, 0],\n",
       "[0, 0, 3]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import diag\n",
    "diag(1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "94fb6ae5-846c-419a-b1b0-694431cfd122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 上三角\n",
    "m = Matrix(2, 2, [1, 2, 0, 1])\n",
    "m.is_upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "db1a03f5-0efe-48d5-81df-520765a287fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & 1\\\\0 & 1 & 1\\\\0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 1, 1],\n",
       "[0, 1, 1],\n",
       "[0, 0, 1]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 上三角\n",
    "from sympy import ones\n",
    "A = ones(3)\n",
    "A.upper_triangular()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4f53f803-f678-4fc7-b66a-ccdfd600586f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & 0\\\\1 & 1 & 0 & 0\\\\1 & 1 & 1 & 0\\\\1 & 1 & 1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0, 0, 0],\n",
       "[1, 1, 0, 0],\n",
       "[1, 1, 1, 0],\n",
       "[1, 1, 1, 1]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 下三角\n",
    "from sympy import ones\n",
    "A = ones(4)\n",
    "A.lower_triangular()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "46963d3a-b511-4847-a2ed-89c93b1c333a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 变形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6f909e45-5f2a-41dc-8ba7-010d4c497241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & 1\\\\1 & 1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 1, 1],\n",
       "[1, 1, 1]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Matrix(2, 3, lambda i, j: 1)\n",
    "m "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "da9869f7-8721-4624-9410-88e16453a319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1 & 1 & 1 & 1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[1, 1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.reshape(1, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4aaf3abe-63d7-4838-a6ba-3215d67bab08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 1\\\\1 & 1\\\\1 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 1],\n",
       "[1, 1],\n",
       "[1, 1]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.reshape(3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ace71ec8-d1ce-476b-a561-7fe65abff96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}a & b\\\\c & d\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[a, b],\n",
       "[c, d]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import Matrix, symbols\n",
    "A = Matrix(2, 2, symbols('a:d'))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a4103f58-ed36-47ce-996e-dabe6956915e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}d & c\\\\b & a\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[d, c],\n",
       "[b, a]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.rot90(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7778fdb3-9a9e-4299-a147-2a6d3ccb8b4b",
   "metadata": {},
   "source": [
    "## 基本运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5aedda03-8a51-4f5f-96b0-041cb00a5c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 24\\\\0 & 15\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 24],\n",
       "[0, 15]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import Matrix\n",
    "\n",
    "A = Matrix([[1, 3], [-2, 3]])\n",
    "B = Matrix([[0, 3], [0, 7]])\n",
    "A + B # 加法\n",
    "A - B # 减法\n",
    "3*A   # 标量乘矩阵\n",
    "A.multiply_elementwise(B) # 逐项积\n",
    "A * B # 矩阵乘法\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6dcbd35a-8efc-4f1a-9877-2fde7f98a89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 24\\\\0 & 15\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 24],\n",
       "[0, 15]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A @ B"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e04dd9-2b14-4b78-8841-751d3b9eaf53",
   "metadata": {},
   "source": [
    "### 加法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c7396bf5-a06d-4dc0-8c2c-77bd3f879c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 6\\\\-2 & 10\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ 1,  6],\n",
       "[-2, 10]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + B"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bf9fcb-746c-48af-b25b-fdf6fc87867e",
   "metadata": {},
   "source": [
    "### 逐项积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a404c009-ca20-469d-972f-326039315083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 10 & 200\\\\300 & 40 & 5\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[  0, 10, 200],\n",
       "[300, 40,   5]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix([[0, 1, 2], [3, 4, 5]])\n",
    "B = Matrix([[1, 10, 100], [100, 10, 1]])\n",
    "A.multiply_elementwise(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a84be2-2de0-4950-9936-2c12d44f5885",
   "metadata": {},
   "source": [
    "## 矩阵乘法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93918b13-e61e-40cf-90c0-3235d55c1278",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = Matrix([0, 1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "be6bcfba-b752-4439-9c3c-6984901f7d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}3\\\\9\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[3],\n",
       "[9]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A*B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e12b8a3d-1215-4431-9b5f-3541910e5fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.25 & -0.75\\\\-0.75 & 1.25\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ 1.25, -0.75],\n",
       "[-0.75,  1.25]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix_2x2 = Matrix([[1.25, -0.75],\n",
    "                     [-0.75, 1.25]])\n",
    "Matrix_2x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e2851410-11d8-4083-929f-4cc58d0b3fa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sympy.matrices.dense.MutableDenseMatrix"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Matrix_2x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8988f5e1-8f56-446f-a1be-6482b480df49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.25 & 0.75\\\\0.75 & 1.25\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1.25, 0.75],\n",
       "[0.75, 1.25]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求逆\n",
    "Matrix_2x2**-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d2d7dfec-076b-4043-bd6f-e02f6f3f3b7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.25 & 0.75\\\\0.75 & 1.25\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1.25, 0.75],\n",
       "[0.75, 1.25]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix_2x2.inv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4f51aa8d-8093-4154-9ad1-563c132e6c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1.25 & -0.75\\\\-0.75 & 1.25\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ 1.25, -0.75],\n",
       "[-0.75,  1.25]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求转置\n",
    "Matrix_2x2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39090b2e-4bf0-44ec-a526-5180fdf40069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.25, -0.75],\n",
       "       [-0.75,  1.25]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将符号矩阵转化为浮点数numpy矩阵\n",
    "import numpy as np\n",
    "\n",
    "np.array(Matrix_2x2).astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ad75fd-a848-4501-b567-2588178392b0",
   "metadata": {},
   "source": [
    "## 符号求逆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "762c778b-8d11-4ed5-8e60-78aad75bfa04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{d}{a d - b c} & - \\frac{b}{a d - b c}\\\\- \\frac{c}{a d - b c} & \\frac{a}{a d - b c}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ d/(a*d - b*c), -b/(a*d - b*c)],\n",
       "[-c/(a*d - b*c),  a/(a*d - b*c)]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import symbols\n",
    "a, b, c, d = symbols('a b c d')\n",
    "A = Matrix([[a, b],\n",
    "            [c, d]])\n",
    "\n",
    "A.inv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ebb1810a-73b2-4e68-90de-0f0a18d2d0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle a d - b c$"
      ],
      "text/plain": [
       "a*d - b*c"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行列式值\n",
    "A.det()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5a46d9a3-4c63-4a5e-bf2f-26dd491823ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle a + d$"
      ],
      "text/plain": [
       "a + d"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方阵的迹\n",
    "A.trace()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b540e851-133b-4f87-947f-669c41b736c1",
   "metadata": {},
   "source": [
    "## 方程组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "047a8901-5d4a-4b1b-9251-71c5d7eda6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}- \\frac{5}{3}\\\\\\frac{7}{3}\\\\\\frac{10}{3}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[-5/3],\n",
       "[ 7/3],\n",
       "[10/3]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix([[1, 2, 0], [3, 1, 2], [0, -1, 1]])\n",
    "b = Matrix([[3], [4], [1]])\n",
    "A.inv() @ b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4f581fce-d717-46ab-a827-e27ea7abbe9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 30 a + \\frac{68 b}{5} - \\frac{74 c}{5}$"
      ],
      "text/plain": [
       "30*a + 68*b/5 - 74*c/5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import Symbol, S\n",
    "\n",
    "a, b, c = Symbol(\"a\"), Symbol(\"b\"), Symbol(\"c\")\n",
    "C = Matrix([[S(1) / 5, 5, 5], [3, 1, 7], [a, b, c]])\n",
    "C.det()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7a8868d9-6e5c-42ca-b5b2-9c9477630ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{- 35 b + 5 c}{150 a + 68 b - 74 c} & \\frac{25 b - 25 c}{150 a + 68 b - 74 c} & - \\frac{444}{- 444 a - \\frac{5032 b}{25} + \\frac{5476 c}{25}}\\\\\\frac{35 a - 15 c}{150 a + 68 b - 74 c} & \\frac{- 25 a + c}{150 a + 68 b - 74 c} & - \\frac{5032}{- 11100 a - 5032 b + 5476 c}\\\\\\frac{- 5 a + 15 b}{150 a + 68 b - 74 c} & \\frac{25 a - b}{150 a + 68 b - 74 c} & - \\frac{74}{150 a + 68 b - 74 c}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[(-35*b + 5*c)/(150*a + 68*b - 74*c), (25*b - 25*c)/(150*a + 68*b - 74*c), -444/(-444*a - 5032*b/25 + 5476*c/25)],\n",
       "[(35*a - 15*c)/(150*a + 68*b - 74*c),   (-25*a + c)/(150*a + 68*b - 74*c),    -5032/(-11100*a - 5032*b + 5476*c)],\n",
       "[(-5*a + 15*b)/(150*a + 68*b - 74*c),    (25*a - b)/(150*a + 68*b - 74*c),             -74/(150*a + 68*b - 74*c)]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.inv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "563fb5a8-c4c1-4046-ac81-49a7b543bbe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "## trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d9cafe51-c51f-41c1-a35f-911158eacb1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 5$"
      ],
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix(2, 2, [1, 2, 3, 4])\n",
    "A.trace()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bbb8d2-fee7-433c-b258-e889c56ac1b7",
   "metadata": {},
   "source": [
    "## 正定性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cbc54213-f4d6-42e9-93bd-f819473abd08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import Matrix, symbols\n",
    "from sympy.plotting import plot3d\n",
    "x1, x2 = symbols('x1 x2')\n",
    "x = Matrix([x1, x2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ae370d-1498-4172-a427-9aabfd4c8826",
   "metadata": {},
   "source": [
    "### 正定"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64fe7a4-d368-4754-a823-830f9add059f",
   "metadata": {},
   "source": [
    "$${\\displaystyle M{\\text{ positive-definite}}\\quad \\iff \\quad \\mathbf {x} ^{\\textsf {T}}M\\mathbf {x} >0{\\text{ for all }}\\mathbf {x} \\in \\mathbb {R} ^{n}\\setminus \\{\\mathbf {0} \\}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "42c1c072-9ba5-4b06-93fe-90dfae06a874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix([[1, 0], [0, 1]])\n",
    "A.is_positive_definite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3e4510c1-88d1-443c-b2fc-67f07cd1a178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}x_{1}^{2} + x_{2}^{2}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[x1**2 + x2**2]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.T*A*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c48df144-4947-4592-8d40-c7489c77bba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x_{1}^{2} + x_{2}^{2}$"
      ],
      "text/plain": [
       "x1**2 + x2**2"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_x = x.T*A*x\n",
    "f_x = f_x[0]\n",
    "f_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "38e94417-37ac-4ce4-8f20-2055e3371078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = plot3d((x.T*A*x)[0, 0], (x1, -1, 1), (x2, -1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772083e4-064a-4fcf-bd33-d913d444e384",
   "metadata": {},
   "source": [
    "### 半正定"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad000b2-9304-499e-abae-d2df12c12dde",
   "metadata": {},
   "source": [
    "$${\\displaystyle M{\\text{ positive semi-definite}}\\quad \\iff \\quad \\mathbf {x} ^{\\textsf {T}}M\\mathbf {x} \\geq 0{\\text{ for all }}\\mathbf {x} \\in \\mathbb {R} ^{n}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e5f091a8-d378-4133-811f-82d2ff58a506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix([[1, -1], [-1, 1]])\n",
    "A.is_positive_definite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "25cfc067-1c47-480b-b724-f0e1cd2ca964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.is_positive_semidefinite"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9da797a-3cd8-4ad0-8cc2-039c906c2fff",
   "metadata": {},
   "source": [
    "### 负定"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be5e050-33c3-4adf-a9f7-a1fbb5157e60",
   "metadata": {},
   "source": [
    "$$ {\\displaystyle M{\\text{ negative-definite}}\\quad \\iff \\quad \\mathbf {x} ^{\\textsf {T}}M\\mathbf {x} <0{\\text{ for all }}\\mathbf {x} \\in \\mathbb {R} ^{n}\\setminus \\{\\mathbf {0} \\}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5cd701c5-7c70-4362-847c-f9678cc1a682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix([[-1, 0], [0, -1]])\n",
    "A.is_negative_definite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "94f9e781-d162-4cf8-9606-654b7c9953d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.is_negative_semidefinite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2a60136a-e683-486f-bf84-a4ca2f22c044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.is_indefinite"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4eec7f-b91f-494f-91c3-47e977e45427",
   "metadata": {},
   "source": [
    "### 半负定"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ac7d4a-0e80-4669-a9d0-4604ef62ae9e",
   "metadata": {},
   "source": [
    "$$ {\\displaystyle M{\\text{ negative semi-definite}}\\quad \\iff \\quad \\mathbf {x} ^{\\textsf {T}}M\\mathbf {x} \\leq 0{\\text{ for all }}\\mathbf {x} \\in \\mathbb {R} ^{n}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81e2e0b-4a3c-4960-bc3b-b91936f85bbe",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c65eaac6-c50a-483a-9c17-b2f4b854e933",
   "metadata": {},
   "source": [
    "### 不定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f86a5111-58fb-4f66-9c07-324b0bd239a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = Matrix([[1, 2], [2, -1]])\n",
    "A.is_indefinite"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a99af1f-b8a6-4aa8-aea6-fa67dbad6b68",
   "metadata": {},
   "source": [
    "## Cholesky分解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "48435c46-b205-488f-bbb9-bdfc474fdfb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}5 & 0 & 0\\\\3 & 3 & 0\\\\-1 & 1 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ 5, 0, 0],\n",
       "[ 3, 3, 0],\n",
       "[-1, 1, 3]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy import Matrix\n",
    "A = Matrix(((25, 15, -5), (15, 18, 0), (-5, 0, 11)))\n",
    "A.cholesky()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "789cd3dd-98aa-46f3-8ad1-6a39fc31b4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}25 & 15 & -5\\\\15 & 18 & 0\\\\-5 & 0 & 11\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[25, 15, -5],\n",
       "[15, 18,  0],\n",
       "[-5,  0, 11]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.cholesky() * A.cholesky().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e47af6-45a7-4d45-a70e-a19df1e702be",
   "metadata": {},
   "source": [
    "## 对角化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5a0da903-ca8b-4a3a-8f1c-12d9f407f966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = Matrix([[1, 2, 0], [0, 3, 0], [2, -4, 2]])\n",
    "M.is_diagonalizable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "70163a70-3a4d-4211-b911-297c793fb3c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = Matrix([[3, -2,  4, -2], [5,  3, -3, -2], [5, -2,  2, -2], [5, -2, -3,  3]])\n",
    "P, D = M.diagonalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ac63e93f-6a53-4163-a218-715f013a7e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 1 & 1 & 0\\\\1 & 1 & 1 & -1\\\\1 & 1 & 1 & 0\\\\1 & 1 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 1, 1,  0],\n",
       "[1, 1, 1, -1],\n",
       "[1, 1, 1,  0],\n",
       "[1, 1, 0,  1]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "213b32ee-b6f9-49a1-8d15-a6d5b8b58ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}-2 & 0 & 0 & 0\\\\0 & 3 & 0 & 0\\\\0 & 0 & 5 & 0\\\\0 & 0 & 0 & 5\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[-2, 0, 0, 0],\n",
       "[ 0, 3, 0, 0],\n",
       "[ 0, 0, 5, 0],\n",
       "[ 0, 0, 0, 5]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e3614c-d0f7-47e3-9647-0ae5a0a7a013",
   "metadata": {},
   "source": [
    "## 特征值分解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f35c1de9-5658-4f30-880e-7fda41d447e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}3 & -2 & 4 & -2\\\\5 & 3 & -3 & -2\\\\5 & -2 & 2 & -2\\\\5 & -2 & -3 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[3, -2,  4, -2],\n",
       "[5,  3, -3, -2],\n",
       "[5, -2,  2, -2],\n",
       "[5, -2, -3,  3]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = Matrix([[3, -2,  4, -2], [5,  3, -3, -2], [5, -2,  2, -2], [5, -2, -3,  3]])\n",
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f82156ca-ada5-4c82-851d-ef40b84a2c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3: 1, -2: 1, 5: 2}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.eigenvals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2d91dcf5-053b-4472-973a-f05a2e7c26c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [0],\n",
       "   [1],\n",
       "   [1],\n",
       "   [1]])]),\n",
       " (3,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [1],\n",
       "   [1],\n",
       "   [1],\n",
       "   [1]])]),\n",
       " (5,\n",
       "  2,\n",
       "  [Matrix([\n",
       "   [1],\n",
       "   [1],\n",
       "   [1],\n",
       "   [0]]),\n",
       "   Matrix([\n",
       "   [ 0],\n",
       "   [-1],\n",
       "   [ 0],\n",
       "   [ 1]])])]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.eigenvects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "eb876a29-e2f9-4b30-bf09-e890319edcee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3: 1, -2: 1, 5: 2}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 对符号数值矩阵求特征值\n",
    "M.eigenvals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "df3f30d5-a185-407d-931e-e9ca820668fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}a^{2} & 2 a b c\\\\2 a b c & b^{2}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[   a**2, 2*a*b*c],\n",
       "[2*a*b*c,    b**2]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Matrix_2x2_abc = Matrix([[a**2, 2*a*b*c],\n",
    "                         [2*a*b*c, b**2]])\n",
    "Matrix_2x2_abc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "43a1f6b4-9756-4e1e-b71f-28bfc4d1889d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{a**2/2 + b**2/2 - sqrt(a**4 + 16*a**2*b**2*c**2 - 2*a**2*b**2 + b**4)/2: 1,\n",
       " a**2/2 + b**2/2 + sqrt(a**4 + 16*a**2*b**2*c**2 - 2*a**2*b**2 + b**4)/2: 1}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix_2x2_abc.eigenvals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "46f3e405-3912-4465-af7b-187b0debfd01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [0],\n",
       "   [1],\n",
       "   [1],\n",
       "   [1]])]),\n",
       " (3,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [1],\n",
       "   [1],\n",
       "   [1],\n",
       "   [1]])]),\n",
       " (5,\n",
       "  2,\n",
       "  [Matrix([\n",
       "   [1],\n",
       "   [1],\n",
       "   [1],\n",
       "   [0]]),\n",
       "   Matrix([\n",
       "   [ 0],\n",
       "   [-1],\n",
       "   [ 0],\n",
       "   [ 1]])])]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.eigenvects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "03a61c6d-60d5-44ed-a9bd-e1dbba2add04",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 向量空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "30ead826-4a06-4741-bbb4-8e9061ce6d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Matrix([\n",
       " [1],\n",
       " [2],\n",
       " [3]]),\n",
       " Matrix([\n",
       " [1],\n",
       " [1],\n",
       " [1]])]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = Matrix([[1, 1, 2], [2 ,1 , 3], [3 , 1, 4]])\n",
    "M.columnspace()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba783c4-7f20-4115-bd95-0919f6e66a9c",
   "metadata": {},
   "source": [
    "## 奇异值分解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eb1bf145-530a-4a4d-ad02-acf9c2349d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import Matrix\n",
    "A = Matrix([[0, 1],[1, 1],[1, 0]])\n",
    "# 奇异值分解\n",
    "U, S, V = A.singular_value_decomposition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6d77c147-89b4-455c-a6f7-acae8f9c62cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{\\sqrt{2}}{2} & \\frac{\\sqrt{6}}{6}\\\\0 & \\frac{\\sqrt{6}}{3}\\\\- \\frac{\\sqrt{2}}{2} & \\frac{\\sqrt{6}}{6}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ sqrt(2)/2, sqrt(6)/6],\n",
       "[         0, sqrt(6)/3],\n",
       "[-sqrt(2)/2, sqrt(6)/6]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ed60f68a-2555-4a52-9e5b-1da1a1c03f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0\\\\0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0],\n",
       "[0, 1]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.T @ U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "59a85ddd-6f26-47dc-9df6-aa601ba87947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0\\\\0 & \\sqrt{3}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1,       0],\n",
       "[0, sqrt(3)]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d1acbd69-c246-4b65-8741-d1e200f2ceab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}- \\frac{\\sqrt{2}}{2} & \\frac{\\sqrt{2}}{2}\\\\\\frac{\\sqrt{2}}{2} & \\frac{\\sqrt{2}}{2}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[-sqrt(2)/2, sqrt(2)/2],\n",
       "[ sqrt(2)/2, sqrt(2)/2]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0831b407-b1df-4a09-a821-8e36f28aefae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0\\\\0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0],\n",
       "[0, 1]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V.T @ V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "911b9ec9-f953-4db2-a30f-6658eb5ce558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0\\\\0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0],\n",
       "[0, 1]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V * V.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "bad0b255-6b4f-4512-865e-64332fa05216",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = A @ A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "da29bce8-ab30-4022-bd9a-674c88fa4ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [ 1],\n",
       "   [-1],\n",
       "   [ 1]])]),\n",
       " (1,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [-1],\n",
       "   [ 0],\n",
       "   [ 1]])]),\n",
       " (3,\n",
       "  1,\n",
       "  [Matrix([\n",
       "   [1],\n",
       "   [2],\n",
       "   [1]])])]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.eigenvects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a78f5ea0-2fc5-492d-85d6-459295154bbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2a76d332-aee9-4e69-b1d7-20b5eac340a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repo: https://github.com/Visualize-ML\n",
    "# Book 1 Python for Beginners  |  From Basic Arithmetic to Machine Learning\n",
    "# Published and copyrighted by Tsinghua University Press\n",
    "# Beijing, China, 2023"
   ]
  }
 ],
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